scholarly journals Development and Evaluation of an In Silico Dermal Absorption Model Relevant for Children

Pharmaceutics ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 172
Author(s):  
Yejin Esther Yun ◽  
Daniella Calderon-Nieva ◽  
Abdullah Hamadeh ◽  
Andrea N. Edginton

The higher skin surface area to body weight ratio in children and the prematurity of skin in neonates may lead to higher chemical exposure as compared to adults. The objectives of this study were: (i) to provide a comprehensive review of the age-dependent anatomical and physiological changes in pediatric skin, and (ii) to construct and evaluate an age-dependent pediatric dermal absorption model. A comprehensive review was conducted to gather data quantifying the differences in the anatomy and physiology of child and adult skin. Maturation functions were developed for model parameters that were found to be age-dependent. A pediatric dermal absorption model was constructed by updating a MoBi implementation of the Dancik et al. 2013 skin permeation model with these maturation functions. Using a workflow for adult-to-child model extrapolation, the predictive performance of the model was evaluated by comparing its predicted rates of flux of diamorphine, phenobarbital and buprenorphine against experimental observations using neonatal skin. For diamorphine and phenobarbital, the model provided reasonable predictions. The ratios of predicted:observed flux in neonates for diamorphine ranged from 0.55 to 1.40. For phenobarbital, the ratios ranged from 0.93 to 1.26. For buprenorphine, the model showed acceptable predictive performance. Overall, the physiologically based pediatric dermal absorption model demonstrated satisfactory prediction accuracy. The prediction of dermal absorption in neonates using a model-based approach will be useful for both drug development and human health risk assessment.

2021 ◽  
Vol 11 (15) ◽  
pp. 6998
Author(s):  
Qiuying Li ◽  
Hoang Pham

Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.


Author(s):  
Imran Shah ◽  
Tia Tate ◽  
Grace Patlewicz

Abstract Motivation Generalized Read-Across (GenRA) is a data-driven approach to estimate physico-chemical, biological or eco-toxicological properties of chemicals by inference from analogues. GenRA attempts to mimic a human expert’s manual read-across reasoning for filling data gaps about new chemicals from known chemicals with an interpretable and automated approach based on nearest-neighbors. A key objective of GenRA is to systematically explore different choices of input data selection and neighborhood definition to objectively evaluate predictive performance of automated read-across estimates of chemical properties. Results We have implemented genra-py as a python package that can be freely used for chemical safety analysis and risk assessment applications. Automated read-across prediction in genra-py conforms to the scikit-learn machine learning library's estimator design pattern, making it easy to use and integrate in computational pipelines. We demonstrate the data-driven application of genra-py to address two key human health risk assessment problems namely: hazard identification and point of departure estimation. Availability and implementation The package is available from github.com/i-shah/genra-py.


2018 ◽  
Vol 52 (6) ◽  
pp. 2433-2456 ◽  
Author(s):  
Ayuna Barlukova ◽  
Diana White ◽  
Gérard Henry ◽  
Stéphane Honoré ◽  
Florence Hubert

Microtubules (MTs) are protein polymers that exhibit a unique type of behavior referred to as dynamic instability. That is, they undergo periods of growth (through the addition of GTP-tubulin) and shortening (through the subtraction of GDP-tubulin). Shortening events are very fast, where this transition is referred to as a catastrophe. There are many processes that regulate MT dynamic instability, however, recent experiments show that MT dynamics may be highly regulated by a MTs age, where young MTs are less likely to undergo shortening events than older ones. In this paper, we develop a novel modeling approach to describe how the age of a MT affects its dynamic properties. In particular, we extend on a previously developed model that describes MT dynamics, by proposing a new concept for GTP-tubulin hydrolysis (the process by which newly incorporated GTP-tubulin is hydrolyzed to lower energy GDP-tubulin). In particular, we assume that hydrolysis is mainly vectorial, age-dependent and delayed according to the GTP-tubulin incorporation into the MT. Through numerical simulation, we are able to show how MT age affects certain properties that define MT dynamics. For example, simulations illustrate how the aging process leads to an increase in the rate of GTP-tubulin hydrolysis for older MTs, as well as increases in catastrophe frequency. Also, since it has been found that MT dynamic instability is affected by chemotherapy microtubule-targeting agents (MTAs), we highlight the fact that our model can be used to investigate the action of MTAs on MT dynamics by varying certain model parameters.


Vibration ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 235-265
Author(s):  
Paul Gardner ◽  
Mattia Dal Borgo ◽  
Valentina Ruffini ◽  
Aidan J. Hughes ◽  
Yichen Zhu ◽  
...  

A digital twin is a powerful new concept in computational modelling that aims to produce a one-to-one mapping of a physical structure, operating in a specific context, into the digital domain. The development of a digital twin provides clear benefits in improved predictive performance and in aiding robust decision making for operators and asset managers. One key feature of a digital twin is the ability to improve the predictive performance over time, via improvements of the digital twin. An important secondary function is the ability to inform the user when predictive performance will be poor. If regions of poor performance are identified, the digital twin must offer a course of action for improving its predictive capabilities. In this paper three sources of improvement are investigated; (i) better estimates of the model parameters, (ii) adding/updating a data-based component to model unknown physics, and (iii) the addition of more physics-based modelling into the digital twin. These three courses of actions (along with taking no further action) are investigated through a probabilistic modelling approach, where the confidence of the current digital twin is used to inform when an action is required. In addition to addressing how a digital twin targets improvement in predictive performance, this paper also considers the implications of utilising a digital twin in a control context, particularly when the digital twin identifies poor performance of the underlying modelling assumptions. The framework is applied to a three-storey shear structure, where the objective is to construct a digital twin that predicts the acceleration response at each of the three floors given an unknown (and hence, unmodelled) structural state, caused by a contact nonlinearity between the upper two floors. This is intended to represent a realistic challenge for a digital twin, the case where the physical twin will degrade with age and the digital twin will have to make predictions in the presence of unforeseen physics at the time of the original model development phase.


Author(s):  
Clifford K. Ho

A probabilistic, transient, three-phase model of chemical transport through human skin has been developed to assess the relative importance of uncertain parameters and processes during chemical exposure assessments and transdermal drug delivery. Penetration routes through the skin that were modeled include the following: (1) intercellular diffusion through the multiphase stratum corneum; (2) aqueous-phase diffusion through sweat ducts; and (3) oil-phase diffusion through hair follicles. Uncertainty distributions were developed for the model parameters, and a Monte Carlo analysis was performed to simulate probability distributions of mass fluxes through each of the routes for a hypothetical scenario of chemical transport through the skin. At early times (60 seconds), the sweat ducts provided a significant amount of simulated mass flux into the bloodstream. At longer times (1 hour), diffusion through the stratum corneum became important because of its relatively large surface area. Sensitivity analyses using stepwise linear regression were also performed to identify model parameters that were most important to the simulated mass fluxes at different times.


Epidemiology ◽  
2006 ◽  
Vol 17 (Suppl) ◽  
pp. S246-S247
Author(s):  
P Beamer ◽  
H Shi ◽  
A Ferguson ◽  
J Leckie

Epidemiology ◽  
2006 ◽  
Vol 17 (Suppl) ◽  
pp. S246
Author(s):  
H Shi ◽  
A C. Ferguson ◽  
P Beamer ◽  
J O. Leckie

Author(s):  
Zhaonan Sun ◽  
Bronislaw Gepner ◽  
Patrick S. Cottler ◽  
Sang-hyun Lee ◽  
Jason Kerrigan

Abstract Mechanical models of adipose tissue are important for various medical applications including cosmetics, injuries, implantable drug delivery systems, and plastic surgeries, and biomechanical applications such as computational human body models for surgery simulation, and blunt impact trauma. This article presents a comprehensive review of experimental approaches that aimed to characterize the mechanical properties of adipose tissue, and the resulting constitutive models and model parameters identified. In particular, this study examines the material behavior of adipose tissue, including its nonlinear stress-strain relationship, viscoelasticity, strain hardening and softening, rate-sensitivity, anisotropy, preconditioning, failure behavior, and temperature dependency.


Metals ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 761 ◽  
Author(s):  
Fei Feng ◽  
Jianjun Li ◽  
Peng Yuan ◽  
Qixian Zhang ◽  
Pan Huang ◽  
...  

An increasing demand exists within the automotive industry to utilize aluminum alloy sheets because of their excellent strength-weight ratio and low emissions, which can improve fuel economy and reduce environmental pollution. High-speed automobile impactions are complicated and highly nonlinear deformation processes. Thus, in this paper, a Gurson-Tvergaard-Needleman (GTN) damage model is used to describe the damage behavior of high-speed electromagnetic impaction to predict the fracture behavior of 5052-O aluminum alloy under high-speed impaction. The parameters of the GTN damage model are obtained based on high-speed electromagnetic forming experiments via scanning electron microscopy. The high-speed electromagnetic impaction behavior process is analyzed according to the obtained GTN model parameters. The shape of the high-speed electromagnetic impaction in the numerical simulations agrees with the experimental results. The analysis of the plastic strain and void volume fraction distributions are analyzed during the process of high-speed impact, which indicates the validity of using the GTN damage model to describe or predict the fracture behavior of high-speed electromagnetic impaction.


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